Data-Driven Moving Horizon Estimation Using Bayesian Optimization

In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization. As long as the measurement data is received, a locally linear dynamics model can be obtained from one Bayesian optimization-based offline learning framework. Herein, the learned model is continuously updated iteratively based on the actual observed data to approximate the actual system dynamic with the intent of minimizing the cost function of the moving horizon estimator until the desired performance is achieved. Meanwhile, the characteristics of Bayesian optimization can guarantee the closest approximation of the learned model to the actual system dynamic. Thus, one effective data-driven moving horizon estimator can be designed further on the basis of this learned model. Finally, the efficiency of the proposed state estimation algorithm is demonstrated by several numerical simulations.Comment: 12 pages,3 figure

Enhancing Control Performance through ESN-Based Model Compensation in MPC for Dynamic Systems

Deriving precise system dynamic models through traditional numerical methods is often a challenging endeavor. The performance of Model Predictive Control is heavily contingent on the accuracy of the system dynamic model. Consequently, this study employs Echo State Networks to acquire knowledge of the unmodeled dynamic characteristics inherent in the system. This information is then integrated with the nominal model, functioning as a form of model compensation. The present paper introduces a control framework that combines ESN with MPC. By perpetually assimilating the disparities between the nominal and real models, control performance experiences augmentation. In a demonstrative example, a second order dynamic system is subjected to simulation. The outcomes conclusively evince that ESNbased MPC adeptly assimilates unmodeled dynamic attributes, thereby elevating the system control proficiency.Comment: 5 pages,3 figures,conferenc

Dynamical localization transition in the non-Hermitian Z2\mathbb{Z}_2 gauge theory

Local constraint in lattice gauge theory provides an exotic mechanism inducing disorder-free localization. However, the nonequilibrium dynamics in the non-Hermition lattice gauge model has not been well understood. Here, we investigate the quench dynamics of spinless fermions with nonreciprocal hopping in the $\mathbb{Z}_2$ gauge field formed from the bond spins. Based on the effective model from duality mapping, the non-Hermitian skin effect, disorder-free localization-delocalization transition, and the real-complex transition of eigenenergies are explored systematically. By identifying the diverse scaling behaviors of quantum mutual information for fermions and spins, we predict that the non-Hermition quantum disentangled liquid presents both in localized and delocalized phase with completely different physical nature, the first comes from the $\mathbb{Z}_2$ gauge field and the second originates from the non-Hermitian skin effect. We finally show that the nonreciprocal dissipation of fermions leads the quantum information flowing from the fermions to spins. Our results provide new insights to the nonequilibrium dynamics in the gauge field, and can be experimentally verified using ultracold atoms in optical lattices.Comment: 13pages, 10 figure

Design and implementation of wire tension measurement system for MWPCs used in the STAR iTPC upgrade

The STAR experiment at RHIC is planning to upgrade the Time Projection Chamber which lies at the heart of the detector. We have designed an instrument to measure the tension of the wires in the multi-wire proportional chambers (MWPCs) which will be used in the TPC upgrade. The wire tension measurement system causes the wires to vibrate and then it measures the fundamental frequency of the oscillation via a laser based optical platform. The platform can scan the entire wire plane, automatically, in a single run and obtain the wire tension on each wire with high precision. In this paper, the details about the measurement method and the system setup will be described. In addition, the test results for a prototype MWPC to be used in the STAR-iTPC upgrade will be presented.Comment: 6 pages, 10 figues, to appear in NIM

Representing Volumetric Videos as Dynamic MLP Maps

This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes. Recent advances in neural scene representations demonstrate their remarkable capability to model and render complex static scenes, but extending them to represent dynamic scenes is not straightforward due to their slow rendering speed or high storage cost. To solve this problem, our key idea is to represent the radiance field of each frame as a set of shallow MLP networks whose parameters are stored in 2D grids, called MLP maps, and dynamically predicted by a 2D CNN decoder shared by all frames. Representing 3D scenes with shallow MLPs significantly improves the rendering speed, while dynamically predicting MLP parameters with a shared 2D CNN instead of explicitly storing them leads to low storage cost. Experiments show that the proposed approach achieves state-of-the-art rendering quality on the NHR and ZJU-MoCap datasets, while being efficient for real-time rendering with a speed of 41.7 fps for $512 \times 512$ images on an RTX 3090 GPU. The code is available at https://zju3dv.github.io/mlp_maps/.Comment: Accepted to CVPR 2023. The first two authors contributed equally to this paper. Project page: https://zju3dv.github.io/mlp_maps

Observation of prolonged coherence time of the collective spin wave of atomic ensemble in a paraffin coated Rb vapor cell

We report a prolonged coherence time of the collective spin wave of a thermal 87Rb atomic ensemble in a paraffin coated cell. The spin wave is prepared through a stimulated Raman Process. The long coherence time time is achieved by prolonging the lifetime of the spins with paraffin coating and minimize dephasing with optimal experimental configuration. The observation of the long time delayed-stimulated Stokes signal in the writing process suggests the prolonged lifetime of the prepared spins; a direct measurement of the decay of anti-Stokes signal in the reading process shows the coherence time is up to 300 us after minimizing dephasing. This is one hundred times longer than the reported coherence time in the similar experiments in thermal atomic ensembles based on the Duan-Lukin-Cirac-Zoller (DLCZ) and its improved protocols. This prolonged coherence time sets the upper limit of the memory time in quantum repeaters based on such protocols, which is crucial for the realization of long-distance quantum communication. The previous reported fluorescence background in the writing process due to collision in a sample cell with buffer gas is also reduced in a cell without buffer gas.Comment: 4 pages, 4 figure

Learning Human Mesh Recovery in 3D Scenes

We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image. Unlike previous methods that perform sceneaware mesh optimization, we propose to first estimate absolute position and dense scene contacts with a sparse 3D CNN, and later enhance a pretrained human mesh recovery network by cross-attention with the derived 3D scene cues. Joint learning on images and scene geometry enables our method to reduce the ambiguity caused by depth and occlusion, resulting in more reasonable global postures and contacts. Encoding scene-aware cues in the network also allows the proposed method to be optimization-free, and opens up the opportunity for real-time applications. The experiments show that the proposed network is capable of recovering accurate and physically-plausible meshes by a single forward pass and outperforms state-of-the-art methods in terms of both accuracy and speed.Comment: Accepted to CVPR 2023. Project page: https://zju3dv.github.io/sahmr